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In spintronics, a perennial goal has been the generation of organic spin-bearing semiconductor materials with magnetic ordering stable at room temperature. To this end, the class of transition metal phthalocyanines has shown much promise in fulfilling this ambition. In particular, alpha-phase cobalt (II) phthalocyanine (α-CoPc) exhibits strong antiferromagnetic exchange interactions producing a long range order up to ∼100 K. However, the underlying mechanism by which this magnetic interaction proceeds is not well understood. In this report, a simple mechanism has been proposed based on the Hubbard Hamiltonian, which elucidates the exchange coupling in α-CoPc. The mechanism provides stipulations for increasing the magnetic coupling, and this directs to a proposal that substitution of the central cobalt ion for rhodium will lead to a significant increase in coupling strength. The strength of this exchange interaction has been evaluated using broken symmetry hybrid exchange density functional theory and indicates that the novel rhodium (II) phthalocyanine system is indeed predicted to exhibit significantly stronger magnetic ordering. This study, therefore, identifies the coupling mechanism in α-CoPc as primarily attributable to kinetic exchange, explains its previously reported strong coupling relative to its first-row transition metal counterparts, and suggests that rhodium (II) phthalocyanine is likely to exhibit stable magnetic ordering at room temperature., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Sprouts are increasingly present in the human diet, being tasty and healthy foods high in antioxidant compounds. Although there is a body of literature on the sprouting of many plant species, Camelina sativa (L.) Crantz has not yet been studied for this purpose. This study aimed to characterize the main bioactive compounds and antioxidant potential of seeds and sprouts of five different Camelina cultivars (ALBA, CO46, CCE43, JOELLE, and VERA). In particular, the contents of phenolic compounds (PCs), phenolic acids (PAs), and glucosinolates (GLSs) were investigated. PCs, PAs, GLSs, and the antioxidant activity of seeds differed among cultivars and were greatly increased by sprouting. A PCA analysis underlined both the effect of the cultivar (PC2) and the germination (PC1) on the nutritional properties of Camelina. The best nutritional properties of seeds were observed for ALBA and CCE43, while the best nutritional properties of sprouts were recorded for CCE43 and JOELLE, since the latter cultivar showed a greater enhancement in phytochemical content and antioxidant activity with sprouting. Finally, a UHPLC-UV procedure for the analysis of GLSs in Camelina was developed and validated. The performance criteria of the proposed method demonstrated that it is useful for the analysis of GLSs in Camelina., Competing Interests: The authors declare no conflict of interest.

A family of boron nitride (BN)-based photocatalysts for solar fuel syntheses have recently emerged. Studies have shown that oxygen doping, leading to boron oxynitride (BNO), can extend light absorption to the visible range. However, the fundamental question surrounding the origin of enhanced light harvesting and the role of specific chemical states of oxygen in BNO photochemistry remains unanswered. Here, using an integrated experimental and first-principles-based computational approach, we demonstrate that paramagnetic isolated OB 3 states are paramount to inducing prominent red-shifted light absorption. Conversely, we highlight the diamagnetic nature of O-B-O states, which are shown to cause undesired larger band gaps and impaired photochemistry. This study elucidates the importance of paramagnetism in BNO semiconductors and provides fundamental insight into its photophysics. The work herein paves the way for tailoring of its optoelectronic and photochemical properties for solar fuel synthesis., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Well designed and optimized epitaxial heterostructures lie at the foundation of materials development for photovoltaic, photocatalytic, and photoelectrochemistry applications. Heterostructure materials offer tunable control over charge separation and transport at the same time preventing recombination of photogenerated excitations at the interface. Thus, it is of paramount importance that a detailed understanding is developed as the basis for further optimization strategies and design. Oxides of copper are nontoxic, low cost, abundant materials with a straightforward and stable manufacturing process. However, in individual applications, they suffer from inefficient charge transport of photogenerated carriers. Hence, in this work, we investigate the role of the interface between epitaxially aligned CuO and Cu 2 O to explore the potential benefits of such an architecture for more efficient electron and hole transfer. The CuO/Cu 2 O heterojunction nature, stability, bonding mechanism, interface dipole, electronic structure, and band bending were rationalized using hybrid density functional theory calculations. New electronic states are identified at the interface itself, which are originating neither from lattice mismatch nor strained Cu-O bonds. They form as a result of a change in coordination environment of CuO surface Cu 2+ cations and an electron transfer across the interface Cu 1+ -O bond. The first process creates occupied defect-like electronic states above the valence band, while the second leaves hole states below the conduction band. These are constitutional to the interface and are highly likely to contribute to recombination effects competing with the improved charged separation from the suitable band bending and alignment and thus would limit the expected output photocurrent and photovoltage. Finally, a favorable effect of interstitial oxygen defects has been shown to allow for band gap tunability at the interface but only to the point of the integral geometrical contact limit of the heterostructure itself.

The electronic and spintronic properties of the monovacancies in freestanding and isotopically compressed graphene are investigated using hybrid exchange density functional perturbation theory. When the effects of electronic self-interaction are taken into account, an integer magnetic moment of 2 μ B is identified for a Jahn-Teller reconstructed V 1 (5-9) monovacancy in freestanding graphene. For graphene with stable ripples induced by a compressive strain of 5%, a bond reconstruction produces a V 1 (55-66) structure for the monovacancy, which is localized at the saddle points of the ripple. The sizeable local distortion induced by reconstruction modifies both the geometric and electronic properties of rippled graphene and quenches the magnetic moment of the vacancy due to the sp 3 hybridization of the central atom. The nonmagnetic V 1 (55-66) structure is found to be stable on rippled structures, with the formation energy ∼2.3 eV lower than that of the metastable distorted V 1 (5-9) structures localized at sites other than the saddle points. The electronic ground state of distorted V 1 (5-9) corresponds to a wide range of fractional magnetic moments (0.50-1.25 μ B ). The computed relative stabilities and the electronic and magnetic properties of the V 1 (5-9) structures are found to be closely related to their local distortions. This analysis of the fundamental properties of defective graphene under compression suggests a number of strategies for generating regular defect patterns with tuneable magnetic and electronic properties and may, therefore, be used as a novel technique to achieve more precise control of graphene electronic structure for various application scenarios such as transistors, strain sensors, and directed chemisorption., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

The effect of zeolite pore geometry and intrinsic acidity on the activation energy of propane monomolecular cracking was investigated for six topologically distinct zeolites with different pore sizes. Periodic density functional theory calculations were used to calculate the activation energy, while cluster models were used to calculate deprotonation energies. The computed intrinsic activation energies showed a smaller variation with topology than the adsorption energies. No correlation was found between the computed deprotonation and ammonia adsorption energies at the acid site and the intrinsic activation energy. Detailed analysis of the computed structures and properties suggests that acid sites with different pore topologies impose geometrical constraints on the ion-pair formed by the ammonium molecule, which differs significantly from those that affect the propane reaction., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene's unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.